The present disclosure relates to infrastructure and operations management and, more specifically, to a system and method for adaptive baseline calculation.
Baseline calculation generally deals with time series analysis in which data is collected and analyzed over time to determine a normal (e.g., accepted, expected, typical) level for a given environment. In known systems and methods, the result of a baseline calculation is used for trend charting and deviation from normal event generation, which alerts users when a currently monitored parameter falls outside of an established, acceptable range.
Existing products in such a performance management space usually have many different options for adjusting the manner of baseline calculation. A primitive case is based on a 30 day moving average. In a more advanced case, users are allowed to specify a time window (e.g., 6 weeks) during which the baseline is calculated using data points corresponding to a particular hour on a specific day of the week (e.g., 11:00 AM on Tuesdays, which would allow averaging over 6 Tuesdays in the case of a 6 week time window). In other cases, vendors allow users to change a granularity of a baseline calculation. For example, a coarse granularity baseline calculation may involve calculating an average temperature for every 1 hour interval over a particular time period, whereas a fine granularity baseline calculation may involve calculating an average temperature for every 5 minute interval over the particular time period. In such other cases, users may make a selection to increase the resolution of the average data over the particular time period by increasing the granularity of the baseline calculation from the coarse granularity to the fine granularity, such that a greater quantity of data points are collected and a greater number of baseline calculations are performed over the particular time period.